```html
<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>基于DJL和Spring Boot的深度学习图像识别应用</title>
    <link href="https://cdn.staticfile.org/font-awesome/6.4.0/css/all.min.css" rel="stylesheet">
    <link href="https://cdn.staticfile.org/tailwindcss/2.2.19/tailwind.min.css" rel="stylesheet">
    <link href="https://fonts.googleapis.com/css2?family=Noto+Serif+SC:wght@400;500;600;700&family=Noto+Sans+SC:wght@300;400;500;700&display=swap" rel="stylesheet">
    <script src="https://cdn.jsdelivr.net/npm/mermaid@latest/dist/mermaid.min.js"></script>
    <style>
        body {
            font-family: 'Noto Sans SC', Tahoma, Arial, Roboto, "Droid Sans", "Helvetica Neue", "Droid Sans Fallback", "Heiti SC", "Hiragino Sans GB", Simsun, sans-serif;
            color: #333;
            line-height: 1.6;
        }
        h1, h2, h3, h4, h5, h6 {
            font-family: 'Noto Serif SC', serif;
            font-weight: 700;
        }
        .hero-gradient {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        }
        .code-block {
            background-color: #2d3748;
            border-left: 4px solid #4299e1;
        }
        .card-hover:hover {
            transform: translateY(-5px);
            box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
        }
        .drop-cap:first-letter {
            float: left;
            font-size: 4.5rem;
            line-height: 0.65;
            margin: 0.1em 0.1em 0.2em 0;
            color: #4299e1;
            font-weight: bold;
        }
    </style>
</head>
<body class="bg-gray-50">
    <!-- Hero Section -->
    <section class="hero-gradient text-white py-20 px-4 md:px-0">
        <div class="container mx-auto max-w-6xl px-4">
            <div class="flex flex-col md:flex-row items-center">
                <div class="md:w-1/2 mb-10 md:mb-0">
                    <h1 class="text-4xl md:text-5xl font-bold leading-tight mb-4">深度学习图像识别</h1>
                    <p class="text-xl md:text-2xl font-light mb-8">使用DJL与Spring Boot构建智能应用</p>
                    <p class="text-lg opacity-90 mb-8">探索如何利用Java生态中的深度学习库实现强大的图像分类功能，只需数行代码即可构建专业级AI应用。</p>
                    <div class="flex space-x-4">
                        <a href="#demo" class="bg-white text-indigo-800 font-semibold px-6 py-3 rounded-lg hover:bg-gray-100 transition duration-300">
                            <i class="fas fa-play-circle mr-2"></i>查看案例
                        </a>
                        <a href="#code" class="border-2 border-white text-white font-semibold px-6 py-3 rounded-lg hover:bg-white hover:bg-opacity-10 transition duration-300">
                            <i class="fas fa-code mr-2"></i>代码解析
                        </a>
                    </div>
                </div>
                <div class="md:w-1/2 flex justify-center">
                    <div class="relative w-full max-w-md">
                        <div class="absolute -top-6 -left-6 w-full h-full border-4 border-white border-opacity-30 rounded-lg"></div>
                        <div class="relative bg-white p-2 rounded-lg shadow-xl">
                            <img src="https://images.unsplash.com/photo-1533416784636-2b0ccfea6b97" alt="AI Vision" class="rounded-md w-full">
                            <div class="p-4">
                                <div class="flex items-center mb-2">
                                    <div class="w-3 h-3 rounded-full bg-green-500 mr-2"></div>
                                    <span class="text-sm font-mono text-gray-700">输出结果:</span>
                                </div>
                                <div class="bg-gray-100 p-3 rounded font-mono text-sm">
                                    <p>1. giraffe: 98.76%</p>
                                    <p>2. zebra: 0.45%</p>
                                    <p>3. deer: 0.32%</p>
                                </div>
                            </div>
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Main Content -->
    <main class="container mx-auto max-w-6xl px-4 py-16">
        <!-- Introduction -->
        <section class="mb-20">
            <div class="drop-cap text-lg leading-relaxed">
                在人工智能和深度学习的世界里，图像识别是最常见且具有广泛应用的一项技术。随着计算能力的提升和深度学习框架的不断进步，图像识别技术已经能够处理从简单对象检测到复杂场景理解的各类任务。在这篇文章中，我们将结合 <span class="font-bold text-indigo-600">DJL (Deep Java Library)</span> 和 <span class="font-bold text-green-600">Spring Boot</span> 框架，展示如何构建一个简单的深度学习应用，让它能够对输入的图像进行分析并提取信息。
            </div>
        </section>

        <!-- Demo Section -->
        <section id="demo" class="mb-20">
            <h2 class="text-3xl font-bold mb-8 border-l-4 border-indigo-500 pl-4">案例功能展示</h2>
            <div class="grid md:grid-cols-3 gap-8 mb-12">
                <div class="bg-white p-6 rounded-xl shadow-md card-hover transition duration-300">
                    <div class="text-indigo-500 mb-4">
                        <i class="fas fa-cloud-download-alt text-3xl"></i>
                    </div>
                    <h3 class="text-xl font-bold mb-3">模型下载</h3>
                    <p>自动下载ResNet18预训练模型及其对应的类别标签文件，简化了模型部署过程。</p>
                </div>
                <div class="bg-white p-6 rounded-xl shadow-md card-hover transition duration-300">
                    <div class="text-blue-500 mb-4">
                        <i class="fas fa-image text-3xl"></i>
                    </div>
                    <h3 class="text-xl font-bold mb-3">图像预处理</h3>
                    <p>完整的图像预处理流水线包括调整大小、中心裁剪、张量转换和标准化处理。</p>
                </div>
                <div class="bg-white p-6 rounded-xl shadow-md card-hover transition duration-300">
                    <div class="text-purple-500 mb-4">
                        <i class="fas fa-brain text-3xl"></i>
                    </div>
                    <h3 class="text-xl font-bold mb-3">模型推理</h3>
                    <p>使用ResNet18模型进行推理，返回分类结果及对应置信度，展示AI的强大识别能力。</p>
                </div>
            </div>

            <div class="bg-white rounded-xl shadow-lg overflow-hidden">
                <div class="bg-gray-800 text-white px-4 py-3 flex items-center">
                    <div class="flex space-x-2 mr-4">
                        <div class="w-3 h-3 rounded-full bg-red-500"></div>
                        <div class="w-3 h-3 rounded-full bg-yellow-500"></div>
                        <div class="w-3 h-3 rounded-full bg-green-500"></div>
                    </div>
                    <span class="font-mono text-sm">示例输出</span>
                </div>
                <div class="p-6">
                    <div class="mermaid">
                        pie
                            title 图像分类结果
                            "长颈鹿: 98.76%" : 98.76
                            "斑马: 0.45%" : 0.45
                            "鹿: 0.32%" : 0.32
                    </div>
                </div>
            </div>
        </section>

        <!-- Code Analysis -->
        <section id="code" class="mb-20">
            <h2 class="text-3xl font-bold mb-8 border-l-4 border-indigo-500 pl-4">代码详细解析</h2>
            
            <div class="mb-12">
                <h3 class="text-2xl font-bold mb-4 text-gray-800 flex items-center">
                    <span class="bg-indigo-100 text-indigo-800 w-8 h-8 rounded-full flex items-center justify-center mr-3">1</span>
                    模型文件下载
                </h3>
                <p class="mb-4">通过<code class="bg-gray-200 px-1 py-0.5 rounded">DownloadUtils</code>从远程仓库下载ResNet18模型和类别标签文件，并存储在本地目录<code class="bg-gray-200 px-1 py-0.5 rounded">build/pytorch_models/resnet18/</code>下。</p>
                <div class="code-block rounded-lg overflow-hidden mb-6">
                    <pre class="text-gray-200 p-4 overflow-x-auto"><code>DownloadUtils.download("https://djl-ai.s3.amazonaws.com/mlrepo/model/cv/image_classification/ai/djl/pytorch/resnet/0.0.1/traced_resnet18.pt.gz",
    "build/pytorch_models/resnet18/resnet18.pt", new ProgressBar());
DownloadUtils.download("https://djl-ai.s3.amazonaws.com/mlrepo/model/cv/image_classification/ai/djl/pytorch/synset.txt",
    "build/pytorch_models/resnet18/synset.txt", new ProgressBar());</code></pre>
                </div>
            </div>

            <div class="mb-12">
                <h3 class="text-2xl font-bold mb-4 text-gray-800 flex items-center">
                    <span class="bg-indigo-100 text-indigo-800 w-8 h-8 rounded-full flex items-center justify-center mr-3">2</span>
                    图像预处理流水线
                </h3>
                <p class="mb-4">使用DJL提供的<code class="bg-gray-200 px-1 py-0.5 rounded">Pipeline</code>构建图像预处理流程，包括调整大小、中心裁剪、张量转换和标准化处理。</p>
                <div class="code-block rounded-lg overflow-hidden mb-6">
                    <pre class="text-gray-200 p-4 overflow-x-auto"><code>Pipeline pipeline = new Pipeline();
pipeline.add(new Resize(256))
        .add(new CenterCrop(224, 224))
        .add(new ToTensor())
        .add(new Normalize(
                new float[]{0.485f, 0.456f, 0.406f},
                new float[]{0.229f, 0.224f, 0.225f}
        ));</code></pre>
                </div>
                <div class="grid md:grid-cols-4 gap-4 mb-4">
                    <div class="bg-white p-4 rounded-lg shadow-sm">
                        <div class="text-blue-500 mb-2">
                            <i class="fas fa-expand-alt"></i>
                        </div>
                        <h4 class="font-semibold">Resize(256)</h4>
                        <p class="text-sm text-gray-600">调整图像大小到256像素</p>
                    </div>
                    <div class="bg-white p-4 rounded-lg shadow-sm">
                        <div class="text-green-500 mb-2">
                            <i class="fas fa-crop-alt"></i>
                        </div>
                        <h4 class="font-semibold">CenterCrop</h4>
                        <p class="text-sm text-gray-600">从中心裁剪224×224区域</p>
                    </div>
                    <div class="bg-white p-4 rounded-lg shadow-sm">
                        <div class="text-purple-500 mb-2">
                            <i class="fas fa-cube"></i>
                        </div>
                        <h4 class="font-semibold">ToTensor</h4>
                        <p class="text-sm text-gray-600">转换图像为张量格式</p>
                    </div>
                    <div class="bg-white p-4 rounded-lg shadow-sm">
                        <div class="text-red-500 mb-2">
                            <i class="fas fa-sliders-h"></i>
                        </div>
                        <h4 class="font-semibold">Normalize</h4>
                        <p class="text-sm text-gray-600">对图像通道进行标准化</p>
                    </div>
                </div>
            </div>

            <div class="mb-12">
                <h3 class="text-2xl font-bold mb-4 text-gray-800 flex items-center">
                    <span class="bg-indigo-100 text-indigo-800 w-8 h-8 rounded-full flex items-center justify-center mr-3">3</span>
                    构建Translator(翻译器)
                </h3>
                <p class="mb-4">翻译器负责数据输入和模型输出之间的转换，输入图像时应用预处理流水线，输出结果时将分类概率应用Softmax转化为可解释的概率分布。</p>
                <div class="code-block rounded-lg overflow-hidden mb-6">
                    <pre class="text-gray-200 p-4 overflow-x-auto"><code>Translator&lt;Image, Classifications&gt; translator = ImageClassificationTranslator.builder()
        .setPipeline(pipeline)
        .optApplySoftmax(true)
        .build();</code></pre>
                </div>
            </div>

            <div class="mb-12">
                <h3 class="text-2xl font-bold mb-4 text-gray-800 flex items-center">
                    <span class="bg-indigo-100 text-indigo-800 w-8 h-8 rounded-full flex items-center justify-center mr-3">4</span>
                    模型推理与结果打印
                </h3>
                <p class="mb-4">核心推理步骤包括加载预训练模型、加载本地图片文件、使用预测器对图片进行推理，并打印分类结果。</p>
                <div class="code-block rounded-lg overflow-hidden mb-6">
                    <pre class="text-gray-200 p-4 overflow-x-auto"><code>ZooModel&lt;Image, Classifications&gt; model = ModelZoo.loadModel(criteria);

File fs = new File("Giraffe.jpg");
Image img = ImageFactory.getInstance().fromInputStream(new FileInputStream(fs));

Predictor&lt;Image, Classifications&gt; predictor = model.newPredictor();
Classifications classifications = predictor.predict(img);

System.out.println(classifications);</code></pre>
                </div>
            </div>
        </section>

        <!-- Dependencies -->
        <section class="mb-20">
            <h2 class="text-3xl font-bold mb-8 border-l-4 border-indigo-500 pl-4">运行环境与依赖</h2>
            <div class="grid md:grid-cols-2 gap-8">
                <div class="bg-white p-6 rounded-xl shadow-md">
                    <h3 class="text-xl font-bold mb-4 text-gray-800 flex items-center">
                        <i class="fas fa-laptop-code text-indigo-500 mr-2"></i>
                        软件依赖
                    </h3>
                    <ul class="space-y-3">
                        <li class="flex items-start">
                            <span class="bg-blue-100 text-blue-800 rounded-full w-6 h-6 flex items-center justify-center mr-3 flex-shrink-0">1</span>
                            <span><strong>JDK 11</strong> 或更高版本</span>
                        </li>
                        <li class="flex items-start">
                            <span class="bg-blue-100 text-blue-800 rounded-full w-6 h-6 flex items-center justify-center mr-3 flex-shrink-0">2</span>
                            <span><strong>Maven</strong> 用于依赖管理</span>
                        </li>
                        <li class="flex items-start">
                            <span class="bg-blue-100 text-blue-800 rounded-full w-6 h-6 flex items-center justify-center mr-3 flex-shrink-0">3</span>
                            <span><strong>DJL 相关依赖</strong>：包括API、PyTorch模型库和引擎</span>
                        </li>
                    </ul>
                </div>
                <div class="bg-white p-6 rounded-xl shadow-md">
                    <h3 class="text-xl font-bold mb-4 text-gray-800 flex items-center">
                        <i class="fas fa-database text-indigo-500 mr-2"></i>
                        数据依赖
                    </h3>
                    <ul class="space-y-3">
                        <li class="flex items-start">
                            <span class="bg-purple-100 text-purple-800 rounded-full w-6 h-6 flex items-center justify-center mr-3 flex-shrink-0">1</span>
                            <span><strong>输入图像</strong>：一张JPEG格式的图片，例如<code class="bg-gray-200 px-1 py-0.5 rounded">Giraffe.jpg</code></span>
                        </li>
                        <li class="flex items-start">
                            <span class="bg-purple-100 text-purple-800 rounded-full w-6 h-6 flex items-center justify-center mr-3 flex-shrink-0">2</span>
                            <span><strong>模型文件</strong>：通过代码自动下载到<code class="bg-gray-200 px-1 py-0.5 rounded">build/pytorch_models/resnet18/</code>目录</span>
                        </li>
                    </ul>
                </div>
            </div>

            <div class="bg-white p-6 rounded-xl shadow-md mt-8">
                <h3 class="text-xl font-bold mb-4 text-gray-800 flex items-center">
                    <i class="fas fa-file-code text-indigo-500 mr-2"></i>
                    Maven依赖配置
                </h3>
                <div class="code-block rounded-lg overflow-hidden">
                    <pre class="text-gray-200 p-4 overflow-x-auto"><code>&lt;properties&gt;
    &lt;djl.version&gt;0.12.0&lt;/djl.version&gt;
&lt;/properties&gt;

&lt;dependencies&gt;
    &lt;dependency&gt;
        &lt;groupId&gt;org.springframework.boot&lt;/groupId&gt;
        &lt;artifactId&gt;spring-boot-starter&lt;/artifactId&gt;
    &lt;/dependency&gt;

    &lt;dependency&gt;
        &lt;groupId&gt;org.springframework.boot&lt;/groupId&gt;
        &lt;artifactId&gt;spring-boot-starter-test&lt;/artifactId&gt;
    &lt;/dependency&gt;

    &lt;dependency&gt;
        &lt;groupId&gt;ai.djl&lt;/groupId&gt;
        &lt;artifactId&gt;api&lt;/artifactId&gt;
        &lt;version&gt;${djl.version}&lt;/version&gt;
    &lt;/dependency&gt;
    &lt;dependency&gt;
        &lt;groupId&gt;ai.djl.pytorch&lt;/groupId&gt;
        &lt;artifactId&gt;pytorch-model-zoo&lt;/artifactId&gt;
        &lt;version&gt;${djl.version}&lt;/version&gt;
    &lt;/dependency&gt;
    &lt;dependency&gt;
        &lt;groupId&gt;ai.djl.pytorch&lt;/groupId&gt;
        &lt;artifactId&gt;pytorch-engine&lt;/artifactId&gt;
        &lt;version&gt;${djl.version}&lt;/version&gt;
        &lt;scope&gt;runtime&lt;/scope&gt;
    &lt;/dependency&gt;
    &lt;dependency&gt;
        &lt;groupId&gt;ai.djl.pytorch&lt;/groupId&gt;
        &lt;artifactId&gt;pytorch-native-auto&lt;/artifactId&gt;
        &lt;version&gt;1.8.1&lt;/version&gt;
    &lt;/dependency&gt;
&lt;/dependencies&gt;</code></pre>
                </div>
            </div>
        </section>

        <!-- Full Code Section -->
        <section class="mb-20">
            <h2 class="text-3xl font-bold mb-8 border-l-4 border-indigo-500 pl-4">完整代码实现</h2>
            <div class="bg-white rounded-xl shadow-lg overflow-hidden">
                <div class="bg-gray-800 text-white px-4 py-3 flex items-center justify-between">
                    <div class="flex items-center">
                        <div class="flex space-x-2 mr-4">
                            <div class="w-3 h-3 rounded-full bg-red-500"></div>
                            <div class="w-3 h-3 rounded-full bg-yellow-500"></div>
                            <div class="w-3 h-3 rounded-full bg-green-500"></div>
                        </div>
                        <span class="font-mono text-sm">ResnetDemo.java</span>
                    </div>
                    <button class="text-sm bg-indigo-600 hover:bg-indigo-700 px-3 py-1 rounded">
                        <i class="fas fa-copy mr-1"></i>复制代码
                    </button>
                </div>
                <div class="code-block rounded-b-lg overflow-hidden">
                    <pre class="text-gray-200 p-4 overflow-x-auto text-sm"><code>package com.ts.javadjl2;

import ai.djl.inference.Predictor;
import ai.djl.modality.Classifications;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.ImageFactory;
import ai.djl.modality.cv.transform.CenterCrop;
import ai.djl.modality.cv.transform.Normalize;
import ai.djl.modality.cv.transform.Resize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.modality.cv.translator.ImageClassificationTranslator;
import ai.djl.repository.zoo.Criteria;
import ai.djl.repository.zoo.ModelZoo;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.DownloadUtils;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.Pipeline;
import ai.djl.translate.Translator;

import java.io.File;
import java.io.FileInputStream;
import java.net.URL;

public class ResnetDemo {
    public static void main(String[] args) throws Exception{
        // 从指定 URL 下载模型文件和类别标签文件，并保存到本地目录
        DownloadUtils.download("https://djl-ai.s3.amazonaws.com/mlrepo/model/cv/image_classification/ai/djl/pytorch/resnet/0.0.1/traced_resnet18.pt.gz",
                "build/pytorch_models/resnet18/resnet18.pt", new ProgressBar());

        // 下载模型文件 ResNet18 的预训练模型
        DownloadUtils.download("https://djl-ai.s3.amazonaws.com/mlrepo/model/cv/image_classification/ai/djl/pytorch/synset.txt",
                "build/pytorch_models/resnet18/synset.txt", new ProgressBar());

        // 定义一个数据预处理流水线 (Pipeline)
        Pipeline pipeline = new Pipeline();
        pipeline.add(new Resize(256)) // 将图像大小调整为 256 像素
                .add(new CenterCrop(224, 224)) // 从图像中央裁剪出 224x224 的区域
                .add(new ToTensor()) // 将图像转换为张量 (Tensor)
                .add(new Normalize(
                        new float[]{0.485f, 0.456f, 0.406f}, // 图像每个通道的均值 (Mean)
                        new float[]{0.229f, 0.224f, 0.225f}  // 图像每个通道的标准差 (StdDev)
                ));

        // 构建一个图像分类的翻译器 (Translator)
        Translator&lt;Image, Classifications&gt; translator = ImageClassificationTranslator.builder()
                .setPipeline(pipeline) // 设置上面定义的预处理流水线
                .optApplySoftmax(true) // 对输出的结果应用 Softmax，使其转化为概率分布
                .build();

        // 设置模型的加载路径（指定本地模型文件的位置）
        System.setProperty("ai.djl.repository.zoo.location", "build/pytorch_models/resnet18");

        // 定义模型加载的标准 (Criteria)
        Criteria&lt;Image, Classifications&gt; criteria = Criteria.builder()
                .setTypes(Image.class, Classifications.class) // 设置输入类型为图像，输出类型为分类结果
                .optArtifactId("ai.djl.localmodelzoo:resnet18") // 指定模型的标识符
                .optTranslator(translator) // 设置翻译器，用于数据预处理和结果转换
                .optProgress(new ProgressBar()) // 显示加载进度条
                .build();

        // 加载预训练模型
        ZooModel&lt;Image, Classifications&gt; model = ModelZoo.loadModel(criteria);

        // 加载本地待预测的图像文件
        File fs = new File("Giraffe.jpg");
        Image img = ImageFactory.getInstance().fromInputStream(new FileInputStream(fs));

        // 使用模型创建预测器 (Predictor)
        Predictor&lt;Image, Classifications&gt; predictor = model.newPredictor();
        Classifications classifications = predictor.predict(img);

        // 输出分类结果
        System.out.println(classifications);
    }
}</code></pre>
                </div>
            </div>
        </section>
    </main>

    <!-- Footer -->
    <footer class="bg-gray-900 text-white py-12">
        <div class="container mx-auto max-w-6xl px-4">
            <div class="flex flex-col md:flex-row justify-between items-center">
                <div class="mb-6 md:mb-0">
                    <h3 class="text-xl font-bold mb-2">技术小馆</h3>
                    <p class="text-gray-400">探索技术前沿，分享开发经验</p>
                </div>
                <div>
                    <a href="http://www.yuque.com/jtostring" class="text-gray-300 hover:text-white transition duration-300 flex items-center">
                        <i class="fas fa-external-link-alt mr-2"></i> http://www.yuque.com/jtostring
                    </a>
                </div>
            </div>
            <div class="border-t border-gray-800 mt-8 pt-8 text-center text-gray-500 text-sm">
                &copy; 2023 技术小馆. 保留所有权利.
            </div>
        </div>
    </footer>

    <script>
        mermaid.initialize({
            startOnLoad: true,
            theme: 'dark',
            fontSize: 16
        });

        // 复制代码功能
        document.querySelectorAll('button').forEach(button => {
            if (button.textContent.includes('复制代码')) {
                button.addEventListener('click', () => {
                    const codeBlock = button.closest('.bg-gray-800').nextElementSibling;
                    const code = codeBlock.querySelector('code').textContent;
                    
                    navigator.clipboard.writeText(code).then(() => {
                        const originalText = button.innerHTML;
                        button.innerHTML = '<i class="fas fa-check mr-1"></i>已复制';
                        setTimeout(() => {
                            button.innerHTML = originalText;
                        }, 2000);
                    });
                });
            }
        });
    </script>
</body>
</html>
```